Introduce RayCudaCommunicator as Ray Compiled Graph communicator

[ruisearch42]

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.

Purpose

Ray uses cupy under the hood for inter-GPU communication in compiled graphs. This dependency is heavy and should be removed.

In this PR, we introduce RayCudaCommunicator which leverages PyNCCL from vLLM. RayCudaCommunicator is registered to Ray Compiled Graph as the communicator to use, therefore cupy dependency can be removed.

Closes #19758

Test Plan

Test with test_pipeline_parallel.py which uses Ray Compiled Graph (and the underlying communicator)

Test Result

Passed

(Optional) Documentation Update

[gemini-code-assist]

Summary of Changes

Hello @ruisearch42, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request establishes a foundational component for enhancing distributed GPU communication within vLLM when leveraging Ray's Compiled Graph capabilities. It introduces RayCudaCommunicator, a specialized class that integrates with Ray's experimental channel API to manage efficient, NCCL-backed data exchange between distributed Ray actors on NVIDIA GPUs. This integration is crucial for optimizing the performance of large-scale model inference and training by streamlining inter-GPU communication.

Highlights

• New Ray Communicator: Introduces RayCudaCommunicator, a new class designed to handle GPU communication for Ray's experimental Compiled Graph feature.
• NCCL-based Communication: The RayCudaCommunicator internally utilizes PyNcclCommunicator to perform efficient GPU-to-GPU data transfers and collective operations like send, recv, allgather, allreduce, and reducescatter.
• Integration with Ray Compiled Graph: The new communicator is registered within ray_distributed_executor.py to be used specifically for CUDA operations when Ray's Compiled Graph is enabled, facilitating optimized distributed execution.

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[gemini-code-assist]

Code Review

This pull request introduces RayCudaCommunicator, a new communicator designed for Ray Compiled Graphs on NVIDIA GPUs, leveraging PyNcclCommunicator for underlying NCCL operations. The changes also include registering this communicator within the Ray distributed executor.

Several areas need attention:

• Critical issues include hardcoded network parameters that will prevent multi-node operation and a missing explicit cleanup for the NCCL communicator.
• Type hint consistency and correctness for list and tuple should be addressed.
• Redundant imports and an unused parameter should be cleaned up.
• Several TODO comments point to important future work, some of which (like performance synchronizations) are significant.
• The PR description template is not filled out, which is important for context and testability.

Overall, the PR lays the groundwork for a specialized Ray communicator, but the critical issues must be resolved before it can be reliably used, especially in distributed environments.

[ruisearch42]

@youkaichao @stephanie-wang Could you help review the PR, thanks!

[kouroshHakha]

realized I didn't release my qs for like 4 days :)

[kouroshHakha]

So I do not thing this class belongs here. Since every other communicator that is here under device_communicators inherits from the CommunicatorBase class and is constructed in a standard way from Platform object.

We can later decide how we need to organize ray stuff, but I think a better organization for now would be vllm/distributed/ray_communicators/cuda.RayCudaCommunicator